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Spatial data warehouses and SOLAP: a new GIS technology

Spatial data warehouses and SOLAP: a new GIS technology. Geosciences, mapping day. Jean-Paul KASPRZYK, phd student. Introduction: GIS and SOLAP. GIS : OnLine Transactional Processing (OLTP) Daily management of spatial data Easy update, integrity and no redundancy of the data

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Spatial data warehouses and SOLAP: a new GIS technology

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  1. Spatial data warehouses and SOLAP: a new GIS technology Geosciences, mapping day Jean-Paul KASPRZYK, phd student

  2. Introduction: GIS and SOLAP • GIS : OnLine Transactional Processing (OLTP) • Daily management of spatial data • Easy update, integrity and no redundancy of the data • Based on transactional databases • Entity association approach • Easy access to data • SOLAP : Spatial OnLine Analytical Processing (Bédard, 1997) • Decision support (Business intelligence) • Archiving  temporal dimension • Based on data warehouses • Multidimensional approach • Easy exploration of the data at different aggregation levels • Integration of large amount of heterogeneous data

  3. Multidimensional approach Time: day Province Commune Commune Time: month Offence Place Offence Place Time: year Offence Place Fact: offence Offender address Serial Commune Commune Snowflake schema of the criminal data Province

  4. SOLAP Architecture ETL Data warehouse Source data Data cube (SOLAP server) OLAP SOLAP

  5. Data integration : ETL (extract transform load)

  6. OLAP interface : drill down Operation drill down

  7. OLAP interface : drill through operation drill through

  8. OLAP interface: slice operation Slice (time) Slice (serie)

  9. OLAP interface: graphic exports

  10. SOLAP Interface: spatial drill down (1) MDX request SELECT {[Measures].[offence]} ON COLUMNS, {[offence place].[province].members} ON ROWS FROM [offence]

  11. SOLAP interface: spatial drill down (2)

  12. SOLAP Interface :

  13. Conclusion • Advantages of SOLAP • Integration and exploration of large amounts of heterogeneous data • Simple interface • Quick requests • Ideal for decision support • Spatial OLAP = new technologies • spatial operations can still be improved • Example: buffer operation through the data cube

  14. Thank you for listening Any question? Jp.kasprzyk@ulg.ac.be

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